- Email: [email protected]
How do China's oil markets affect other commodity markets both domestically and internationally?
ARTICLE IN PRESS
JID: FRL
[m3Gsc;August 12, 2016;15:51]
Finance Research Letters 0 0 0 (2016) 1–8
Contents lists available at ScienceDirect
Finance Research Letters journal homepage: www.elsevier.com/locate/frl
How do China’s oil markets affect other commodity markets both domestically and internationally? Qiang Ji a,∗, Ying Fan b a
Center for Energy and Environmental Policy research, Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China b School of Economics & Management, Beihang University, Beijing 100191, China
a r t i c l e
i n f o
Article history: Received 8 June 2016 Revised 17 July 2016 Accepted 10 August 2016 Available online xxx JEL codes: G15 Q4
a b s t r a c t This study investigates the contemporaneous causality between China’s oil markets with other commodity markets both domestically and internationally using an error correction model combined with a directed acyclic graph technique. The results indicate that China’s oil markets are cointegrated with other commodity markets both domestically and internationally. The impact of China’s fuel oil futures market on other domestic commodity markets is great when oil prices are high, but the influence is comparatively weak when oil prices are lower. However, due to the lack of futures market, China’s crude oil market has little influence on other commodity markets at any time. © 2016 Elsevier Inc. All rights reserved.
Keywords: Information transmission Directed acyclic graph method Commodity market
1. Introduction Since China commenced its reform and open policies in the late 1970 s, an ongoing transition from a planned economy to a market economy has occurred. Reform of market prices, especially oil prices, has become an important part of its economic system. Along with China’s oil marketization process, the information linkage between China’s oil market and the international oil market has become closer. On one hand, China’s oil price has kept pace with international oil price and influenced greatly by international price volatility; on the other hand, 2008 financial crisis brings new opportunities for China’s oil market development. It’s because OECD countries represented by US undergoes a severe economic recession and weak oil demand since 2008, the highly increasing oil demand in China makes the main contributions to support the international oil price. In the meantime, oil markets are associated with other commodity markets in a series of ways. First, the development of liquid biofuels, such as bioethanol and biodiesel has strengthened the price comovement between oil and agriculture market (Chang and Su, 2010). In addition, the soaring oil prices can also influence agriculture commodity prices through cost-push effects by increasing cost of production (Nazlioglu and Soytas, 2012). Second, during the oil refining process, price changes of oil can also affect metal prices through supply and demand chains (Baffes, 2007). Finally, global market integration and feasible electronic trading system also boost traders to employ portfolio strategies to reduce market risk.
∗
Corresponding author. E-mail address: [email protected] (Q. Ji).
http://dx.doi.org/10.1016/j.frl.2016.08.009 1544-6123/© 2016 Elsevier Inc. All rights reserved.
Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
ARTICLE IN PRESS
JID: FRL 2
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
Especially, financialisation of the commodity market has accelerated this process. Therefore, research on the comovement between oil market and other commodity market has been paid more attention in the field of energy economics. Since 2003, China’s commodity futures markets have undergone a great boom covering various fields such as energy, agricultural products and metals. The information transmission between China’s commodity markets and international commodity markets has strengthened. However, the research between Chinese and international commodity markets is still limited and the existence of information transmission between them needs to be further verified. Therefore, an understanding of the information transmission between China’s oil markets and international commodity markets is very important for perfecting the market system in China and for providing an insight into international economic rules. The relationship between the international oil market and other commodity markets has been studied by many researchers (Pindyck and Rotemberg, 1990; Baffes, 2007; Sieczka and Holyst, 2009). Price comovement and cointegration between oil prices and metal prices, such as gold, copper and silver has been investigated using various methodologies, including copulas, correlation method, cointegration and generalized impulse-response and causality approach (Harri et al., 2009; Nazlioglu and Soytas, 2011; Nazlioglu, 2011; Reboredo and Ugolini, 2016). Price spillover and information transmission between oil prices and agriculture prices has also been analyzed by many researchers. Their methodologies mainly focus on classic econometric approaches and mathematical model including GARCH-type models, panel cointegration, the generalized forecast error variance decompositions and wavelet approach (Hammoudeh and Yuan, 2008; Sari et al., 2010; Nazlioglu and Soytas, 2012; Ji and Fan, 2016; Ftiti et al., 2016). Most studies have verified the strong linkages between oil and other commodity markets. However, literature on the interaction between China’s oil market and both domestic and international commodity markets is still limited. Some research has investigated whether China’s oil price is involved in the world oil market (Chen et al., 2009; Liu et al., 2013; Ji and Fan, 2016), while research on the comovement between China’s oil market and other commodity markets is basically blank. The only research is that Wu and Li (2013) investigate the volatility spillover among China’s crude oil, corn and fuel ethanol markets using BEKK-MVGARCH model. Therefore, this paper chooses China’s oil markets for analysis and comparison of the characteristics of information transmission to and from domestic and international other commodity markets, which complements the current research. The main contributions of the paper are as follows. (1) We investigate the long-run relationship between China’s oil markets and other commodity markets before and after the 2008 financial crisis using an error correction model (ECM). (2) A directed acyclic graph (DAG) technique is firstly employed to identify the contemporaneous causal structure. Then, the different characteristics of information transmission between China’s oil markets and domestic and foreign commodity markets are further investigated. 2. Model for information transmission 2.1. Error correction model Let Xt denote a vector of commodity prices. Assuming that commodity price series are integrated of the same order (details on tests are in Section 3.1), the corresponding ECM is specified as follows:
Xt = Xt−1 +
k−1
i Xt−i + μ + εt
(t = 1, 2, . . . , T ),
(1)
i=1
where is the difference operator (Xt = Xt –Xt–1 ). is a coefficient matrix = αβ , where β is the cointegrating vector and α indicates the speed of adjustment to the previous period’s deviation from the cointegrating relationship. i is a matrix of short-run dynamic coefficients, μ is a vector of intercepts, and ε t is a vector of innovations. In general, the commonly used Granger test can only identify lag causality and does not explain contemporaneous causal relations. Therefore, variance decomposition of ECM (Sims, 1980) is used to explore the dynamic structure. However, the traditional variance decomposition method usually adopted Cholesky factorization, which involves a recursive contemporaneous causal structure assumption. The Cholesky decomposition restricts that later variables cannot cause contemporaneous changes in the previous variables (Ji, 2012). That is to say the result of variance decomposition is completely decided by the variables’ ordering assumed in the VECM (Swanson and Granger, 1997). Moreover, economic theories rarely provide guidance for contemporaneous causal ordering and most assumptions are only based on subjective settings. Therefore, how to restrict appropriate contemporaneous causal patterns is the key step in variance decomposition. The DAG approach employed in this paper is fully data driven and can overcome the unrealistic assumption of a recursive structure in the Cholesky decomposition and the shortage of structural factorization (Cody and Mills, 1991). 2.2. DAG theory The DAG method constructs contemporaneous causal relations by examining the correlation structure of the VAR model introduced by Sprites et al. (20 0 0). This method is widely applied to economic and financial fields (Awokuse and Bessler, 20 03; Bessler and Yang, 20 03; Ji, 2012). In this paper, we attempt to use this method to identify the contemporaneous Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
ARTICLE IN PRESS
JID: FRL
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
[m3Gsc;August 12, 2016;15:51] 3
causality among commodity markets. A DAG is essentially an assignment of the contemporaneous causal flow among variables based on unconditional and partial correlations. Therefore, it can objectively identify the contemporaneous causality. The algorithm has two main steps. 1 A complete undirected graph is built in which all variables are linked (a vertex denotes the ECM residual for a variable). The unconditional correlation matrix is calculated, and edges are removed from the undirected graph if the unconditional correlation for the variables is not significantly different from zero. 2 First-order partial correlation is tested for the remaining edges, and edges connecting two variables whose first-order partial correlation is not statistically different from zero are removed. Edges that survive the first-order test are then tested by second-order partial correlation, and so on. For N variables, the algorithm continues until no edge exists or N–2 order partial correlation tests have been carried out. Then a new undirected graph is obtained using the two-step tests and the remaining edges are identified using the notion of a separate set and the DAG is confirmed (detailed information see Ji, 2012). In applications, Fisher’s z statistic is used to test whether conditional correlations are significantly different from zero as follows:
z(ρ (i, j|k ), n ) =
1 2
n − |k| − 3 ln
1 + ρ (i, j|k ) , 1 − ρ (i, j|k )
(2)
where ρ (i, j׀k) is the population correlation between variable i and j conditional on variables k, and ׀k ׀is the number of variables in k (Bessler and Yang, 2003, Yang et al., 2006). 3. Empirical results 3.1. Data Representative commodities in Chinese and international futures markets were selected for analysis. In Chinese commodity market, due to lack of crude oil futures, Daqing crude oil spot price is selected (CCO). In addition, fuel oil futures price (CFO), soybean futures (CSoybean), corn futures (CCorn), copper futures (CCopper) and gold futures (CGold) are selected. Correspondingly, in international commodity markets, WTI futures (CO) and fuel oil futures (FO) from NYMEX; soybean (Soybean) and corn (Corn) futures from CBOT; and copper (Copper) and gold (Gold) futures from COMEX are selected. To avoid nonsynchronous trading and day-of-the-week effects, all the data are weekly log returns over the period from 24 September 2004 to 24 September 2010 according to data availability. To explore the influence of the 2008 global financial crisis, the sample data were divided into two intervals. Since the crisis spread to commodity markets up to July 2008, we selected a pre-crisis sample of 24 September 2004–25 July 2008 and a post-crisis sample of 1 August 2008–24 September 2010. Summary statistics for logarithm returns for commodity markets are presented in Table 1. The direction for China’s commodity market returns is always consistent with the international markets, and the financial crisis had a great negative impact on returns. The mean return for each commodity is positive before the crisis and changes to negative after the crisis, except for gold markets owing to risk avoidance and hedging. A comparison of domestic and foreign commodity markets reveals that the mean for Chinese commodity market returns is lower than for the corresponding international commodity market before and after the crisis. The standard deviation shows a similar phenomenon, and reflects the situation whereby China’s commodity market is not fully open and speculation is weak relative to international markets. Finally, all the returns exhibit relatively high kurtosis with a fat tail and a non-normal distribution, as verified by the Jarque–Bera test. 3.2. Unit root and cointegration tests An Augmented Dickey-Fuller test was applied for the unit root test of each variable. From Table 2, all the variables are integrated of order one, I(1). The Johansen cointegration results in Table 2 suggest that a long-run relationship among markets exists and an error correction model for commodity markets is constructed. According to the results of cointegration test, the cointegrating vector is likely to be a linear combination of a subset of the 12 variables. Therefore, it is necessary to confirm whether every single variable is included into the integration vector and whether it is responsive to the fluctuation of that. To answer this question, two steps are used to re-estimate the structure of the cointegration vector for ECM (Bessler and Yang, 2003). 1) To test whether each variable is in the cointegration vector, the ECM is re-estimated while restricting the value of β to zero. From Table 3, before the crisis, only CBOT Soybean is not in the cointegration vector, while all the variables are in the cointegration vector after the crisis. 2) To test the response to the deviation of each variable from the cointegration vector (weak exogeneity), a similar test is applied that places restrictions on the speed-of-adjustment parameter α . From Table 4, before the crisis, only COMEX Gold and CBOT Corn cannot adjust after disturbed by shocks. After the crisis, only NYMEX CO and COMEX Gold cannot reject the null hypothesis. Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
ARTICLE IN PRESS
JID: FRL 4
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
Table 1 Descriptive statistics for weekly log return for each commodity marketa . Commodity market
Denotation
Before the crisis NYMEX crude oil Daqing crude oil NYMEX fuel oil Shanghai fuel oil CBOT soybean Dalian soybean CBOT corn Dalian corn COMEX gold Shanghai gold COMEX copper Shanghai copper
CO CCO FO CFO Soybean CSoybean Corn CCorn Gold CGold Copper CCopper
After the crisis NYMEX crude oil Daqing crude oil NYMEX fuel oil Shanghai fuel oil CBOT soybean Dalian soybean CBOT corn Dalian corn COMEX gold Shanghai gold COMEX copper Shanghai copper
CO CCO FO CFO Soybean CSoybean Corn CCorn Gold CGold Copper CCopper
Mean
Std. Dev.
Skewness
Kurtosis
Jarque–Bera
0.506 0.463 0.522 0.474 0.512 0.279 0.538 0.244 0.427 0.331 0.521 0.378
3.579 3.479 3.891 3.775 3.568 2.763 4.359 2.196 2.674 2.453 4.355 3.822
−0.303 −1.408 −0.039 0.185 −0.552 −0.586 0.148 2.342 −0.678 −0.133 −0.109 0.073
2.318 9.917 4.740 4.470 4.177 5.395 4.814 15.281 3.796 5.618 4.088 5.083
6.667∗ b 424.323∗∗ 8.589∗ 18.380∗∗ 20.837∗∗ 56.891∗∗ 27.022∗∗ 1382.12∗∗ 19.786∗∗ 55.426∗∗ 9.861∗∗ 34.879∗∗
−0.466 −0.439 −0.455 −0.171 −0.173 −0.065 −0.074 −0.037 0.325 0.294 −0.101 −0.031
6.207 5.411 5.409 5.255 6.373 3.112 6.085 1.943 3.350 3.278 6.647 5.309
−0.698 −0.205 −0.353 −2.482 −0.923 −2.381 −0.930 1.306 −0.060 −0.823 −2.065 −0.758
4.893 3.608 4.601 17.345 7.883 15.549 7.361 15.108 4.587 5.628 11.279 5.422
25.125∗∗ 34.595∗∗ 13.906∗∗ 1046.47∗∗ 123.758∗∗ 818.14∗∗ 102.088∗∗ 696.82∗∗ 11.503∗∗ 43.667∗∗ 388.75∗∗ 37.074∗∗
Notes: a Returns for commodity prices were calculated as 100∗ ln (pricet /pricet- 1 ). b ∗ and ∗∗ denote significance at the 5% and 1% levels, respectively.
Table 2 Unit root and cointegration tests. Unit root test Pre-crisis variable
Cointegration test ADF t-statistic
Post-crisis variable
ADF t-statistic
CO CO CCO CCO FO FO CFO CFO Soybean Soybean CSoybean CSoybean
−0.843 −11.343∗∗ −1.674 −10.010∗∗ −0.838 −12.042∗∗ −1.548 −16.312∗∗ −1.409 −12.878∗∗ −2.187 −11.876∗∗
CO CO CCO CCO FO FO CFO CFO Soybean Soybean CSoybean CSoybean
−1.837 −9.301∗∗ a −1.455 −7.704∗∗ −2.014 −9.237∗∗ −0.427 −10.401∗∗ −0.741 −14.878∗∗ −0.439 −11.179∗∗
Corn Corn CCorn CCorn Gold Gold CGold CGold Copper Copper CCopper CCopper
−2.027 −12.129∗∗ −2.697 −13.443∗∗ −2.653 −15.541∗∗ −2.823 −14.056∗∗ −2.136 −15.112∗∗ −1.620 −12.288∗∗
Corn Corn CCorn CCorn Gold Gold CGold CGold Copper Copper CCopper CCopper
−2.126 −6.761∗∗ −3.391 −10.228∗∗ −0.356 −10.151∗∗ −0.525 −9.980∗∗ −3.288 −10.900∗∗ −3.040 −9.147∗∗
Before the crisis Hypothesized No. of CE(s)
Trace statistic
C(5%)b
Dc
None∗ At most 1∗ At most 2∗ At most 3 At most 4 At most 5 At most 6 At most 7 At most 8 – After the crisis
421.35 326.49 245.12 178.25 137.23 101.95 67.76 42.71 26.12 –
334.98 285.14 239.24 197.37 159.53 125.62 95.75 69.82 47.86 –
R R R F F F F F F F
Hypothesized No. of CE(s)
Trace Statistic
C(5%)
D
None∗ At most At most At most At most At most At most At most At most –
600.43 460.33 352.62 245.41 185.47 132.64 91.82 63.85 39.54 –
334.98 285.14 239.24 197.37 159.53 125.62 95.75 69.82 47.86 –
R R R R R R F F F F
1∗ 2∗ 3∗ 4∗ 5∗ 6 7 8
Note: a ∗ b c
and ∗∗ denote significance at the 5% and 1% levels, respectively. C(5%) denote critical value of trace test at 5% significant level. “D” gives decision to reject (R) or fail to reject (F) at 5% significant level.
Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
ARTICLE IN PRESS
JID: FRL
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
5
Table 3 Tests on exclusion of each variable from the cointegration vector. k
χ 2 statistic (before the crisis) CO
CCO
FO ∗∗ a
CFO
Soybean
CSoybean
Corn
CCorn
Gold
CGold ∗
Copper
CCopper
1 2 3 D k
0.13 12.3 1.58 24.8∗∗ 12.1∗∗ 6.36∗ ∗∗ ∗∗ 33.6 26.6∗∗ 15.9 Rb R R χ 2 statistic (after the crisis)
1.37 14.5∗∗ 24.2∗∗ R
0.12 2.01 2.08 F
3.17 7.94∗ 9.56∗ R
0.28 10.4∗∗ 11.5∗∗ R
0.31 10.3∗∗ 20.3∗∗ R
3.42 4.37 18.3∗∗ R
4.03 4.88 13.5∗∗ R
0.37 0.55 20.1∗∗ R
0.81 0.95 15.0∗∗ R
1 2 3 4 5 6 D
3.60 3.67 45.3∗∗ 46.5∗∗ 47.5∗∗ 59.5∗∗ R
4.70∗ 4.71 37.4∗∗ 38.3∗∗ 44.7∗∗ 48.6∗∗ R
0.05 0.52 37.4∗∗ 38.8∗∗ 47.3∗∗ 50.8∗∗ R
0.02 0.43 24.1∗∗ 42.7∗∗ 52.9∗∗ 62.2∗∗ R
0.00 0.31 25.1∗∗ 32.1∗∗ 42.1∗∗ 51.8∗∗ R
30.7∗∗ 30.8∗∗ 37.1∗∗ 56.9∗∗ 57.0∗∗ 57.3∗∗ R
24.0∗∗ 24.3∗∗ 25.1∗∗ 64.5∗∗ 75.8∗∗ 79.8∗∗ R
1.21 1.69 23.7∗∗ 23.8∗∗ 28.8∗∗ 41.6∗∗ R
0.85 1.32 25.2∗∗ 25.2∗∗ 30.7∗∗ 43.5∗∗ R
1.70 2.01 23.7∗∗ 36.9∗∗ 48.6∗∗ 60.5∗∗ R
2.05 2.29 25.2∗∗ 36.4∗∗ 48.1∗∗ 60.2∗∗ R
1.53 1.96 24.1∗∗ 26.4∗∗ 28.8∗∗ 32.3∗∗ R
Notes: a ∗ and ∗∗ denote significance at the 5% and 1% levels, respectively. b “D” gives decision to reject (R) or fail to reject (F) at 5% significant level. Table 4 Test of weak exogeneity of variables. k
χ 2 statistic (before the crisis) CO
CCO
FO ∗∗ a
1 2 3 D
0.002 1.27 10.69∗ Rb
7.18 10.51∗∗ 27.59∗∗ R
k
χ 2 statistic (after the crisis)
1 2 3 4 5 6 D
0.04 0.36 3.21 3.60 6.88 6.89 F
5.55∗ 5.69 7.97∗ 8.11 15.37∗∗ 18.78∗∗ R
0.15 1.99 10.76∗ R
0.05 0.08 5.95 5.96 11.97∗ 13.33∗ R
CFO
Soybean ∗∗
CSoybean ∗∗
Corn
CCorn
Gold
CGold
Copper
CCopper
8.20 11.42∗∗ 15.00∗∗ R
3.15 6.00∗ 8.47∗ R
8.81 12.93∗∗ 14.28∗∗ R
0.02 0.96 7.13 F
0.59 6.76∗ 19.81∗∗ R
0.14 6.49 7.22 F
0.09 7.60∗ 10.87∗ R
0.00 0.34 9.04∗ R
3.29 4.93 7.95∗ R
0.15 0.59 13.39∗∗ 14.64∗∗ 23.46∗∗ 23.51∗∗ R
2.51 2.90 7.13 9.55∗ 16.02∗∗ 19.49∗∗ R
0.40 0.56 3.26 8.16 16.16∗∗ 16.74∗ R
1.03 1.50 17.87∗∗ 18.72∗∗ 19.35∗∗ 21.59∗∗ R
1.14 1.15 9.67∗ 14.76∗∗ 25.07∗∗ 29.80∗∗ R
0.17 0.72 1.52 1.75 7.23 7.92 F
1.36 1.54 4.81 4.83 16.45∗∗ 21.91∗∗ R
0.25 0.64 6.55 12.57∗ 18.15∗∗ 25.51∗∗ R
0.29 0.29 4.48 7.97 9.98 17.70∗∗ R
Notes: a ∗ and ∗∗ denote significance at the 5% and 1% levels, respectively. b “D” gives decision to reject(R) or fail to reject (F) at 5% significant level.
Finally, based on the results in Tables 3 and 4, associated restrictions on both α and β are tested. The χ 2 statistic for the corresponding hypothesis before and after the crisis is 9.58 (0.14) and 17.31 (0.07), respectively, so the null hypothesis cannot be rejected at the 5% significance level. Therefore, restrictions on α and β in our specification are reasonable. 3.3. Contemporaneous information transmission The contemporaneous causal structures between China’s oil markets and other commodity markets before and after the crisis are constructed based on the above restrictions shown in Table 3 and Table 4 using the PC algorithm (Sprites et al., 20 0 0). The directed edges in Fig. 1 reveal that the international oil market had great contemporaneous effects on China’s oil market before the crisis. For further explanation, the international crude oil price has a direct influence on China’s crude oil price, similar to the direct effect of the international fuel oil price on China’s fuel oil price. Moreover, in the international oil market, the crude oil price influences the fuel oil price, whereas the fuel oil price affects the crude oil price in the Chinese oil market. In terms of contemporaneous causality between China’s oil markets and other domestic commodity markets, China’s fuel oil price has a direct influence on domestic corn and gold markets and an indirect influence on the copper market through the gold market, whereas China’s crude oil market does not have such an impact. These results indicate that from an empirical view, Chinese fuel oil futures have a great influence on domestic commodity markets, whereas the impact of the crude oil market is limited only as a price taker (there are no directed edges oriented from CCO). This performance may be related to the lack of a crude oil futures market in China. On the contrary, Chinese fuel oil futures traded on the Shanghai Futures Exchange with a larger trading volume would have an impact on domestic commodity markets. In addition, among Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
JID: FRL 6
ARTICLE IN PRESS
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
Fig. 1. Directed acyclic graph before the crisis.
Fig. 2. Directed acyclic graph after the crisis.
all the markets, the international crude oil market is least influenced by other markets (there is no directed edge headed by CO) and has the most influence on other markets (most of the directed edges and paths are oriented from CO). Moreover, before the crisis, except for the soybean market, the international market for a specific commodity has a direct influence on the corresponding Chinese market, which reflects the characteristics of international commodity price changes followed by domestic market prices. Viewed from the virtual frame in Fig. 1, the interior direct and indirect links are presented in agriculture and metal markets, which indicate that the similar commodities has strong price linkage and are easily affected by each other due to their similar properties. In addition, there is no direct link between agriculture and metal markets. Fig. 2 reveals that contemporaneous causality among commodity markets changed greatly after the crisis, with some new characteristics apparent. 1. The Chinese oil market began to have an influence on the international oil market compared to the situation before the crisis. In particular, the Chinese fuel oil market had a direct influence on the international crude oil market, while the influence of the international crude oil price on China’s oil market disappeared. This indicates that the influence of increased oil demand in China on the international crude oil price strengthened owing to weak oil demand for OECD countries, whereas the sensitivity of China’s oil market on the price volatility of international crude oil decreased because of government regulation to reduce risk. Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
JID: FRL
ARTICLE IN PRESS
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
7
2. The contemporaneous impact of the Chinese fuel oil market on domestic commodity markets disappeared, which is similar to the impact of the international oil market on international commodity markets. The role of the international crude oil market changed from a price leader to a price taker, and the domestic and international copper and gold markets had direct and indirect impacts on it. It is clear from Fig. 2 that there is no directed edge from the four oil markets to agriculture or metal markets, which indicates that the impact of oil markets on other commodity markets significantly decreased when oil prices were lower. From this point of the view, domestic and international oil markets have the same characteristics. 3. The influence of domestic commodity markets on the Chinese crude oil market increased. After the crisis, Chinese soybean, corn, copper and gold markets had a direct or indirect impact on the Chinese crude oil market; in particular, a contemporaneous causality relationship between Chinese soybean and crude oil markets always exists. The reason is that China is a major producer and the largest importer of soybean, and soybean oil has a substitution effect on diesel as a raw material for biofuel. After the crisis, the decrease in biofuel cost affected by the decrease in soybean prices influenced the crude oil price. The significant impact of other domestic commodity markets on the crude oil market indicates that the stability of Chinese commodity markets was weakened by the financial crisis and the regulation policy exerted on one market may induce a similar reaction for other markets; in other words, oriented policies among the markets are obvious. However, only the Chinese crude oil spot market shows a price response lag in relation to the futures market. Thus, the Chinese crude oil price is influenced by other domestic futures markets. 4. The impacts of domestic commodity markets on international commodity markets strengthened, especially for agriculture and metal markets. Moreover, the internal links among agriculture markets strengthened while it is polarization in the metal markets. The relationship between copper and gold markets disappeared owing to differences in feedback to the financial crisis: the price of copper decreased because it is a common metal, but the price of gold increased as a hedging tool. In addition, the information transmission among markets strengthened after the crisis, which is reflected in a stronger relationship between agriculture and metal markets which does not exist before the crisis. By comparison, the Chinese crude oil market is always a price taker and is mainly affected by the international oil market, which is determined by the pricing mechanism for the market itself. However, the degree of marketization is relatively high for the Chinese fuel oil futures market and it has similar characteristics to international oil markets; the impact on domestic commodity markets is greater when oil prices were high before the crisis, compared to a weak impact when prices were lower after the crisis. 4. Conclusions This study revealed the impact of Chinese oil markets on domestic and international commodity markets by identifying information transmission among them. Some important conclusions can be drawn. The Chinese oil market is no longer fully closed; with the stronger degree of marketization, the relationship with international commodity markets has become closer. In particular, development of China’s oil futures market has led to a long-term equilibrium relationship between the Chinese oil market and other international and domestic commodity markets. This indicates that as global market integration has accelerated and commodity market mechanisms have improved, not only has the linkage of different domestic commodity markets been enhanced, but the relationship between domestic and international commodity markets has also become closer. In addition, the performance of China’s fuel oil and crude oil markets is not entirely consistent, and both have their own characteristics in terms of information transmission to and from other international and domestic commodity markets. China’s crude oil market is a price taker before and after the crisis and is directly affected by the international oil market. In contrast, China’s fuel oil market is not only influenced by international oil markets, but also has an important effect on China’s other commodity markets. The contrastive results indicates that the liquidity of China’s crude oil market is relatively lower owing to the lack of a futures market; thus, it is difficult to play a role in the international pricing mechanism and the impact on other commodity markets is also weak. Acknowledgements Supports from the National Natural Science Foundation of China under Grant No. 91546109, No. 71133005, No. 71203210 and No. 71503114 are acknowledged. The authors appreciate the weekly seminars at CEEP in CAS, from where the earlier draft of the paper got improved. References Awokuse, T.O., Bessler, D.A., 2003. Vector autoregression, policy analysis, and directed graphs: an application to the US economy. J. Appl. Econ. 6, 1–24. Baffes, J., 2007. Oil spills on other commodities. Resour. Policy 32, 126–134. Bessler, D.A., Yang, J., 2003. The structure of interdependence in international stock markets. J. Int. Money Finance 22 (2), 261–287. Chang, T.H., Su, H.M., 2010. The substitutive effect of biofuels on fossil fuels in the lower and higher crude oil price periods. Energy 35, 2807–2813. Chen, K.C., Chen, S.L., Wu, L.F., 2009. Price causal relations between China and the world oil markets. Global Finance J. 20 (2), 107–118. Cody, B.J., Mills, L.O., 1991. The role of commodity prices in formulating monetary policy. Rev. Econ. Stat. 73, 358–365.
Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
JID: FRL 8
ARTICLE IN PRESS
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
Ftiti, Z., Fatnassi, I., Tiwari, A.K., 2016. Neoclassical finance, behavioral finance and noise traders: assessment of gold–oil markets. Finance Res. Lett. 17, 33–40. Hammoudeh, S., Yuan, Y., 2008. Metal volatility in presence of oil and interest rate shocks. Energy Econ. 30, 606–620. Harri, A., Nalley, L., Hudson, D., 2009. The relationship between oil, exchange rates and commodity prices. J. Agr. Appl. Econ. 41 (2), 501–510. Ji, Q., 2012. System analysis approach for the identification of factors driving crude oil prices. Comput. Indus. Eng. 63 (3), 615–625. Ji, Q., Fan, Y., 2016. Evolution of the world crude oil market integration: A graph theory analysis. Energy Econ. 53, 90–100. Liu, L., Chen, C.C., Wan, J., 2013. Is world oil market “one great pool”? An example from China’s and international oil markets. Econ. Modell. 35, 364–373. Nazlioglu, S., 2011. World oil and agricultural commodity prices: evidence from nonlinear causality. Energy Policy 39, 2935–2943. Nazlioglu, S., Soytas, U., 2011. World oil prices and agricultural commodity prices: evidence from an emerging market. Energy Econ. 33, 488–496. Nazlioglu, S., Soytas, U., 2012. Oil price, agricultural commodity prices, and the dollar: a panel cointegration and causality analysis. Energy Econ. 34, 1098–1104. Pindyck, R., Rotemberg, J., 1990. The excess comovement of commodity prices. Econ. J. 100, 1173–1189. Reboredo, J.C., Ugolini, A., 2016. The impact of downward/upward oil price movements on metal prices. Resour. Policy 49, 129–141. Sari, R., Hammoudeh, S., Soytas, U., 2010. Dynamics of oil price, precious metal prices, and exchange rate. Energy Econ. 32, 351–362. Sieczka, P., Holyst, J.A., 2009. Correlations in commodity markets. Physica A 388, 1621–1630. Sims, C., 1980. Macroeconomics and reality. Econometrica 48, 1–48. Sprites, P., Glymour, C., Scheines, R., 20 0 0. Causation, Prediction, and Search. MIT Press, Cambridge, pp. 1–130. Swanson, N., Granger, C., 1997. Impulse response functions based on a causal approach to residual Orthogonalization in Vector Autoregression. J. Am. Stat. Assoc. 92, 357–367. Wu, H, Li, S, 2013. Volatility spillovers in China’s crude oil, corn and fuel ethanol markets. Energy Policy 62, 878–886. Yang, J., Guo, H., Wang, Z.J., 2006. International transmission of inflation among G-7 countries: a data-determined VAR analysis. J. Bank. Finance 30 (10), 2681–2700.
Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
JID: FRL
[m3Gsc;August 12, 2016;15:51]
Finance Research Letters 0 0 0 (2016) 1–8
Contents lists available at ScienceDirect
Finance Research Letters journal homepage: www.elsevier.com/locate/frl
How do China’s oil markets affect other commodity markets both domestically and internationally? Qiang Ji a,∗, Ying Fan b a
Center for Energy and Environmental Policy research, Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China b School of Economics & Management, Beihang University, Beijing 100191, China
a r t i c l e
i n f o
Article history: Received 8 June 2016 Revised 17 July 2016 Accepted 10 August 2016 Available online xxx JEL codes: G15 Q4
a b s t r a c t This study investigates the contemporaneous causality between China’s oil markets with other commodity markets both domestically and internationally using an error correction model combined with a directed acyclic graph technique. The results indicate that China’s oil markets are cointegrated with other commodity markets both domestically and internationally. The impact of China’s fuel oil futures market on other domestic commodity markets is great when oil prices are high, but the influence is comparatively weak when oil prices are lower. However, due to the lack of futures market, China’s crude oil market has little influence on other commodity markets at any time. © 2016 Elsevier Inc. All rights reserved.
Keywords: Information transmission Directed acyclic graph method Commodity market
1. Introduction Since China commenced its reform and open policies in the late 1970 s, an ongoing transition from a planned economy to a market economy has occurred. Reform of market prices, especially oil prices, has become an important part of its economic system. Along with China’s oil marketization process, the information linkage between China’s oil market and the international oil market has become closer. On one hand, China’s oil price has kept pace with international oil price and influenced greatly by international price volatility; on the other hand, 2008 financial crisis brings new opportunities for China’s oil market development. It’s because OECD countries represented by US undergoes a severe economic recession and weak oil demand since 2008, the highly increasing oil demand in China makes the main contributions to support the international oil price. In the meantime, oil markets are associated with other commodity markets in a series of ways. First, the development of liquid biofuels, such as bioethanol and biodiesel has strengthened the price comovement between oil and agriculture market (Chang and Su, 2010). In addition, the soaring oil prices can also influence agriculture commodity prices through cost-push effects by increasing cost of production (Nazlioglu and Soytas, 2012). Second, during the oil refining process, price changes of oil can also affect metal prices through supply and demand chains (Baffes, 2007). Finally, global market integration and feasible electronic trading system also boost traders to employ portfolio strategies to reduce market risk.
∗
Corresponding author. E-mail address: [email protected] (Q. Ji).
http://dx.doi.org/10.1016/j.frl.2016.08.009 1544-6123/© 2016 Elsevier Inc. All rights reserved.
Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
ARTICLE IN PRESS
JID: FRL 2
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
Especially, financialisation of the commodity market has accelerated this process. Therefore, research on the comovement between oil market and other commodity market has been paid more attention in the field of energy economics. Since 2003, China’s commodity futures markets have undergone a great boom covering various fields such as energy, agricultural products and metals. The information transmission between China’s commodity markets and international commodity markets has strengthened. However, the research between Chinese and international commodity markets is still limited and the existence of information transmission between them needs to be further verified. Therefore, an understanding of the information transmission between China’s oil markets and international commodity markets is very important for perfecting the market system in China and for providing an insight into international economic rules. The relationship between the international oil market and other commodity markets has been studied by many researchers (Pindyck and Rotemberg, 1990; Baffes, 2007; Sieczka and Holyst, 2009). Price comovement and cointegration between oil prices and metal prices, such as gold, copper and silver has been investigated using various methodologies, including copulas, correlation method, cointegration and generalized impulse-response and causality approach (Harri et al., 2009; Nazlioglu and Soytas, 2011; Nazlioglu, 2011; Reboredo and Ugolini, 2016). Price spillover and information transmission between oil prices and agriculture prices has also been analyzed by many researchers. Their methodologies mainly focus on classic econometric approaches and mathematical model including GARCH-type models, panel cointegration, the generalized forecast error variance decompositions and wavelet approach (Hammoudeh and Yuan, 2008; Sari et al., 2010; Nazlioglu and Soytas, 2012; Ji and Fan, 2016; Ftiti et al., 2016). Most studies have verified the strong linkages between oil and other commodity markets. However, literature on the interaction between China’s oil market and both domestic and international commodity markets is still limited. Some research has investigated whether China’s oil price is involved in the world oil market (Chen et al., 2009; Liu et al., 2013; Ji and Fan, 2016), while research on the comovement between China’s oil market and other commodity markets is basically blank. The only research is that Wu and Li (2013) investigate the volatility spillover among China’s crude oil, corn and fuel ethanol markets using BEKK-MVGARCH model. Therefore, this paper chooses China’s oil markets for analysis and comparison of the characteristics of information transmission to and from domestic and international other commodity markets, which complements the current research. The main contributions of the paper are as follows. (1) We investigate the long-run relationship between China’s oil markets and other commodity markets before and after the 2008 financial crisis using an error correction model (ECM). (2) A directed acyclic graph (DAG) technique is firstly employed to identify the contemporaneous causal structure. Then, the different characteristics of information transmission between China’s oil markets and domestic and foreign commodity markets are further investigated. 2. Model for information transmission 2.1. Error correction model Let Xt denote a vector of commodity prices. Assuming that commodity price series are integrated of the same order (details on tests are in Section 3.1), the corresponding ECM is specified as follows:
Xt = Xt−1 +
k−1
i Xt−i + μ + εt
(t = 1, 2, . . . , T ),
(1)
i=1
where is the difference operator (Xt = Xt –Xt–1 ). is a coefficient matrix = αβ , where β is the cointegrating vector and α indicates the speed of adjustment to the previous period’s deviation from the cointegrating relationship. i is a matrix of short-run dynamic coefficients, μ is a vector of intercepts, and ε t is a vector of innovations. In general, the commonly used Granger test can only identify lag causality and does not explain contemporaneous causal relations. Therefore, variance decomposition of ECM (Sims, 1980) is used to explore the dynamic structure. However, the traditional variance decomposition method usually adopted Cholesky factorization, which involves a recursive contemporaneous causal structure assumption. The Cholesky decomposition restricts that later variables cannot cause contemporaneous changes in the previous variables (Ji, 2012). That is to say the result of variance decomposition is completely decided by the variables’ ordering assumed in the VECM (Swanson and Granger, 1997). Moreover, economic theories rarely provide guidance for contemporaneous causal ordering and most assumptions are only based on subjective settings. Therefore, how to restrict appropriate contemporaneous causal patterns is the key step in variance decomposition. The DAG approach employed in this paper is fully data driven and can overcome the unrealistic assumption of a recursive structure in the Cholesky decomposition and the shortage of structural factorization (Cody and Mills, 1991). 2.2. DAG theory The DAG method constructs contemporaneous causal relations by examining the correlation structure of the VAR model introduced by Sprites et al. (20 0 0). This method is widely applied to economic and financial fields (Awokuse and Bessler, 20 03; Bessler and Yang, 20 03; Ji, 2012). In this paper, we attempt to use this method to identify the contemporaneous Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
ARTICLE IN PRESS
JID: FRL
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
[m3Gsc;August 12, 2016;15:51] 3
causality among commodity markets. A DAG is essentially an assignment of the contemporaneous causal flow among variables based on unconditional and partial correlations. Therefore, it can objectively identify the contemporaneous causality. The algorithm has two main steps. 1 A complete undirected graph is built in which all variables are linked (a vertex denotes the ECM residual for a variable). The unconditional correlation matrix is calculated, and edges are removed from the undirected graph if the unconditional correlation for the variables is not significantly different from zero. 2 First-order partial correlation is tested for the remaining edges, and edges connecting two variables whose first-order partial correlation is not statistically different from zero are removed. Edges that survive the first-order test are then tested by second-order partial correlation, and so on. For N variables, the algorithm continues until no edge exists or N–2 order partial correlation tests have been carried out. Then a new undirected graph is obtained using the two-step tests and the remaining edges are identified using the notion of a separate set and the DAG is confirmed (detailed information see Ji, 2012). In applications, Fisher’s z statistic is used to test whether conditional correlations are significantly different from zero as follows:
z(ρ (i, j|k ), n ) =
1 2
n − |k| − 3 ln
1 + ρ (i, j|k ) , 1 − ρ (i, j|k )
(2)
where ρ (i, j׀k) is the population correlation between variable i and j conditional on variables k, and ׀k ׀is the number of variables in k (Bessler and Yang, 2003, Yang et al., 2006). 3. Empirical results 3.1. Data Representative commodities in Chinese and international futures markets were selected for analysis. In Chinese commodity market, due to lack of crude oil futures, Daqing crude oil spot price is selected (CCO). In addition, fuel oil futures price (CFO), soybean futures (CSoybean), corn futures (CCorn), copper futures (CCopper) and gold futures (CGold) are selected. Correspondingly, in international commodity markets, WTI futures (CO) and fuel oil futures (FO) from NYMEX; soybean (Soybean) and corn (Corn) futures from CBOT; and copper (Copper) and gold (Gold) futures from COMEX are selected. To avoid nonsynchronous trading and day-of-the-week effects, all the data are weekly log returns over the period from 24 September 2004 to 24 September 2010 according to data availability. To explore the influence of the 2008 global financial crisis, the sample data were divided into two intervals. Since the crisis spread to commodity markets up to July 2008, we selected a pre-crisis sample of 24 September 2004–25 July 2008 and a post-crisis sample of 1 August 2008–24 September 2010. Summary statistics for logarithm returns for commodity markets are presented in Table 1. The direction for China’s commodity market returns is always consistent with the international markets, and the financial crisis had a great negative impact on returns. The mean return for each commodity is positive before the crisis and changes to negative after the crisis, except for gold markets owing to risk avoidance and hedging. A comparison of domestic and foreign commodity markets reveals that the mean for Chinese commodity market returns is lower than for the corresponding international commodity market before and after the crisis. The standard deviation shows a similar phenomenon, and reflects the situation whereby China’s commodity market is not fully open and speculation is weak relative to international markets. Finally, all the returns exhibit relatively high kurtosis with a fat tail and a non-normal distribution, as verified by the Jarque–Bera test. 3.2. Unit root and cointegration tests An Augmented Dickey-Fuller test was applied for the unit root test of each variable. From Table 2, all the variables are integrated of order one, I(1). The Johansen cointegration results in Table 2 suggest that a long-run relationship among markets exists and an error correction model for commodity markets is constructed. According to the results of cointegration test, the cointegrating vector is likely to be a linear combination of a subset of the 12 variables. Therefore, it is necessary to confirm whether every single variable is included into the integration vector and whether it is responsive to the fluctuation of that. To answer this question, two steps are used to re-estimate the structure of the cointegration vector for ECM (Bessler and Yang, 2003). 1) To test whether each variable is in the cointegration vector, the ECM is re-estimated while restricting the value of β to zero. From Table 3, before the crisis, only CBOT Soybean is not in the cointegration vector, while all the variables are in the cointegration vector after the crisis. 2) To test the response to the deviation of each variable from the cointegration vector (weak exogeneity), a similar test is applied that places restrictions on the speed-of-adjustment parameter α . From Table 4, before the crisis, only COMEX Gold and CBOT Corn cannot adjust after disturbed by shocks. After the crisis, only NYMEX CO and COMEX Gold cannot reject the null hypothesis. Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
ARTICLE IN PRESS
JID: FRL 4
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
Table 1 Descriptive statistics for weekly log return for each commodity marketa . Commodity market
Denotation
Before the crisis NYMEX crude oil Daqing crude oil NYMEX fuel oil Shanghai fuel oil CBOT soybean Dalian soybean CBOT corn Dalian corn COMEX gold Shanghai gold COMEX copper Shanghai copper
CO CCO FO CFO Soybean CSoybean Corn CCorn Gold CGold Copper CCopper
After the crisis NYMEX crude oil Daqing crude oil NYMEX fuel oil Shanghai fuel oil CBOT soybean Dalian soybean CBOT corn Dalian corn COMEX gold Shanghai gold COMEX copper Shanghai copper
CO CCO FO CFO Soybean CSoybean Corn CCorn Gold CGold Copper CCopper
Mean
Std. Dev.
Skewness
Kurtosis
Jarque–Bera
0.506 0.463 0.522 0.474 0.512 0.279 0.538 0.244 0.427 0.331 0.521 0.378
3.579 3.479 3.891 3.775 3.568 2.763 4.359 2.196 2.674 2.453 4.355 3.822
−0.303 −1.408 −0.039 0.185 −0.552 −0.586 0.148 2.342 −0.678 −0.133 −0.109 0.073
2.318 9.917 4.740 4.470 4.177 5.395 4.814 15.281 3.796 5.618 4.088 5.083
6.667∗ b 424.323∗∗ 8.589∗ 18.380∗∗ 20.837∗∗ 56.891∗∗ 27.022∗∗ 1382.12∗∗ 19.786∗∗ 55.426∗∗ 9.861∗∗ 34.879∗∗
−0.466 −0.439 −0.455 −0.171 −0.173 −0.065 −0.074 −0.037 0.325 0.294 −0.101 −0.031
6.207 5.411 5.409 5.255 6.373 3.112 6.085 1.943 3.350 3.278 6.647 5.309
−0.698 −0.205 −0.353 −2.482 −0.923 −2.381 −0.930 1.306 −0.060 −0.823 −2.065 −0.758
4.893 3.608 4.601 17.345 7.883 15.549 7.361 15.108 4.587 5.628 11.279 5.422
25.125∗∗ 34.595∗∗ 13.906∗∗ 1046.47∗∗ 123.758∗∗ 818.14∗∗ 102.088∗∗ 696.82∗∗ 11.503∗∗ 43.667∗∗ 388.75∗∗ 37.074∗∗
Notes: a Returns for commodity prices were calculated as 100∗ ln (pricet /pricet- 1 ). b ∗ and ∗∗ denote significance at the 5% and 1% levels, respectively.
Table 2 Unit root and cointegration tests. Unit root test Pre-crisis variable
Cointegration test ADF t-statistic
Post-crisis variable
ADF t-statistic
CO CO CCO CCO FO FO CFO CFO Soybean Soybean CSoybean CSoybean
−0.843 −11.343∗∗ −1.674 −10.010∗∗ −0.838 −12.042∗∗ −1.548 −16.312∗∗ −1.409 −12.878∗∗ −2.187 −11.876∗∗
CO CO CCO CCO FO FO CFO CFO Soybean Soybean CSoybean CSoybean
−1.837 −9.301∗∗ a −1.455 −7.704∗∗ −2.014 −9.237∗∗ −0.427 −10.401∗∗ −0.741 −14.878∗∗ −0.439 −11.179∗∗
Corn Corn CCorn CCorn Gold Gold CGold CGold Copper Copper CCopper CCopper
−2.027 −12.129∗∗ −2.697 −13.443∗∗ −2.653 −15.541∗∗ −2.823 −14.056∗∗ −2.136 −15.112∗∗ −1.620 −12.288∗∗
Corn Corn CCorn CCorn Gold Gold CGold CGold Copper Copper CCopper CCopper
−2.126 −6.761∗∗ −3.391 −10.228∗∗ −0.356 −10.151∗∗ −0.525 −9.980∗∗ −3.288 −10.900∗∗ −3.040 −9.147∗∗
Before the crisis Hypothesized No. of CE(s)
Trace statistic
C(5%)b
Dc
None∗ At most 1∗ At most 2∗ At most 3 At most 4 At most 5 At most 6 At most 7 At most 8 – After the crisis
421.35 326.49 245.12 178.25 137.23 101.95 67.76 42.71 26.12 –
334.98 285.14 239.24 197.37 159.53 125.62 95.75 69.82 47.86 –
R R R F F F F F F F
Hypothesized No. of CE(s)
Trace Statistic
C(5%)
D
None∗ At most At most At most At most At most At most At most At most –
600.43 460.33 352.62 245.41 185.47 132.64 91.82 63.85 39.54 –
334.98 285.14 239.24 197.37 159.53 125.62 95.75 69.82 47.86 –
R R R R R R F F F F
1∗ 2∗ 3∗ 4∗ 5∗ 6 7 8
Note: a ∗ b c
and ∗∗ denote significance at the 5% and 1% levels, respectively. C(5%) denote critical value of trace test at 5% significant level. “D” gives decision to reject (R) or fail to reject (F) at 5% significant level.
Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
ARTICLE IN PRESS
JID: FRL
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
5
Table 3 Tests on exclusion of each variable from the cointegration vector. k
χ 2 statistic (before the crisis) CO
CCO
FO ∗∗ a
CFO
Soybean
CSoybean
Corn
CCorn
Gold
CGold ∗
Copper
CCopper
1 2 3 D k
0.13 12.3 1.58 24.8∗∗ 12.1∗∗ 6.36∗ ∗∗ ∗∗ 33.6 26.6∗∗ 15.9 Rb R R χ 2 statistic (after the crisis)
1.37 14.5∗∗ 24.2∗∗ R
0.12 2.01 2.08 F
3.17 7.94∗ 9.56∗ R
0.28 10.4∗∗ 11.5∗∗ R
0.31 10.3∗∗ 20.3∗∗ R
3.42 4.37 18.3∗∗ R
4.03 4.88 13.5∗∗ R
0.37 0.55 20.1∗∗ R
0.81 0.95 15.0∗∗ R
1 2 3 4 5 6 D
3.60 3.67 45.3∗∗ 46.5∗∗ 47.5∗∗ 59.5∗∗ R
4.70∗ 4.71 37.4∗∗ 38.3∗∗ 44.7∗∗ 48.6∗∗ R
0.05 0.52 37.4∗∗ 38.8∗∗ 47.3∗∗ 50.8∗∗ R
0.02 0.43 24.1∗∗ 42.7∗∗ 52.9∗∗ 62.2∗∗ R
0.00 0.31 25.1∗∗ 32.1∗∗ 42.1∗∗ 51.8∗∗ R
30.7∗∗ 30.8∗∗ 37.1∗∗ 56.9∗∗ 57.0∗∗ 57.3∗∗ R
24.0∗∗ 24.3∗∗ 25.1∗∗ 64.5∗∗ 75.8∗∗ 79.8∗∗ R
1.21 1.69 23.7∗∗ 23.8∗∗ 28.8∗∗ 41.6∗∗ R
0.85 1.32 25.2∗∗ 25.2∗∗ 30.7∗∗ 43.5∗∗ R
1.70 2.01 23.7∗∗ 36.9∗∗ 48.6∗∗ 60.5∗∗ R
2.05 2.29 25.2∗∗ 36.4∗∗ 48.1∗∗ 60.2∗∗ R
1.53 1.96 24.1∗∗ 26.4∗∗ 28.8∗∗ 32.3∗∗ R
Notes: a ∗ and ∗∗ denote significance at the 5% and 1% levels, respectively. b “D” gives decision to reject (R) or fail to reject (F) at 5% significant level. Table 4 Test of weak exogeneity of variables. k
χ 2 statistic (before the crisis) CO
CCO
FO ∗∗ a
1 2 3 D
0.002 1.27 10.69∗ Rb
7.18 10.51∗∗ 27.59∗∗ R
k
χ 2 statistic (after the crisis)
1 2 3 4 5 6 D
0.04 0.36 3.21 3.60 6.88 6.89 F
5.55∗ 5.69 7.97∗ 8.11 15.37∗∗ 18.78∗∗ R
0.15 1.99 10.76∗ R
0.05 0.08 5.95 5.96 11.97∗ 13.33∗ R
CFO
Soybean ∗∗
CSoybean ∗∗
Corn
CCorn
Gold
CGold
Copper
CCopper
8.20 11.42∗∗ 15.00∗∗ R
3.15 6.00∗ 8.47∗ R
8.81 12.93∗∗ 14.28∗∗ R
0.02 0.96 7.13 F
0.59 6.76∗ 19.81∗∗ R
0.14 6.49 7.22 F
0.09 7.60∗ 10.87∗ R
0.00 0.34 9.04∗ R
3.29 4.93 7.95∗ R
0.15 0.59 13.39∗∗ 14.64∗∗ 23.46∗∗ 23.51∗∗ R
2.51 2.90 7.13 9.55∗ 16.02∗∗ 19.49∗∗ R
0.40 0.56 3.26 8.16 16.16∗∗ 16.74∗ R
1.03 1.50 17.87∗∗ 18.72∗∗ 19.35∗∗ 21.59∗∗ R
1.14 1.15 9.67∗ 14.76∗∗ 25.07∗∗ 29.80∗∗ R
0.17 0.72 1.52 1.75 7.23 7.92 F
1.36 1.54 4.81 4.83 16.45∗∗ 21.91∗∗ R
0.25 0.64 6.55 12.57∗ 18.15∗∗ 25.51∗∗ R
0.29 0.29 4.48 7.97 9.98 17.70∗∗ R
Notes: a ∗ and ∗∗ denote significance at the 5% and 1% levels, respectively. b “D” gives decision to reject(R) or fail to reject (F) at 5% significant level.
Finally, based on the results in Tables 3 and 4, associated restrictions on both α and β are tested. The χ 2 statistic for the corresponding hypothesis before and after the crisis is 9.58 (0.14) and 17.31 (0.07), respectively, so the null hypothesis cannot be rejected at the 5% significance level. Therefore, restrictions on α and β in our specification are reasonable. 3.3. Contemporaneous information transmission The contemporaneous causal structures between China’s oil markets and other commodity markets before and after the crisis are constructed based on the above restrictions shown in Table 3 and Table 4 using the PC algorithm (Sprites et al., 20 0 0). The directed edges in Fig. 1 reveal that the international oil market had great contemporaneous effects on China’s oil market before the crisis. For further explanation, the international crude oil price has a direct influence on China’s crude oil price, similar to the direct effect of the international fuel oil price on China’s fuel oil price. Moreover, in the international oil market, the crude oil price influences the fuel oil price, whereas the fuel oil price affects the crude oil price in the Chinese oil market. In terms of contemporaneous causality between China’s oil markets and other domestic commodity markets, China’s fuel oil price has a direct influence on domestic corn and gold markets and an indirect influence on the copper market through the gold market, whereas China’s crude oil market does not have such an impact. These results indicate that from an empirical view, Chinese fuel oil futures have a great influence on domestic commodity markets, whereas the impact of the crude oil market is limited only as a price taker (there are no directed edges oriented from CCO). This performance may be related to the lack of a crude oil futures market in China. On the contrary, Chinese fuel oil futures traded on the Shanghai Futures Exchange with a larger trading volume would have an impact on domestic commodity markets. In addition, among Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
JID: FRL 6
ARTICLE IN PRESS
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
Fig. 1. Directed acyclic graph before the crisis.
Fig. 2. Directed acyclic graph after the crisis.
all the markets, the international crude oil market is least influenced by other markets (there is no directed edge headed by CO) and has the most influence on other markets (most of the directed edges and paths are oriented from CO). Moreover, before the crisis, except for the soybean market, the international market for a specific commodity has a direct influence on the corresponding Chinese market, which reflects the characteristics of international commodity price changes followed by domestic market prices. Viewed from the virtual frame in Fig. 1, the interior direct and indirect links are presented in agriculture and metal markets, which indicate that the similar commodities has strong price linkage and are easily affected by each other due to their similar properties. In addition, there is no direct link between agriculture and metal markets. Fig. 2 reveals that contemporaneous causality among commodity markets changed greatly after the crisis, with some new characteristics apparent. 1. The Chinese oil market began to have an influence on the international oil market compared to the situation before the crisis. In particular, the Chinese fuel oil market had a direct influence on the international crude oil market, while the influence of the international crude oil price on China’s oil market disappeared. This indicates that the influence of increased oil demand in China on the international crude oil price strengthened owing to weak oil demand for OECD countries, whereas the sensitivity of China’s oil market on the price volatility of international crude oil decreased because of government regulation to reduce risk. Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
JID: FRL
ARTICLE IN PRESS
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
7
2. The contemporaneous impact of the Chinese fuel oil market on domestic commodity markets disappeared, which is similar to the impact of the international oil market on international commodity markets. The role of the international crude oil market changed from a price leader to a price taker, and the domestic and international copper and gold markets had direct and indirect impacts on it. It is clear from Fig. 2 that there is no directed edge from the four oil markets to agriculture or metal markets, which indicates that the impact of oil markets on other commodity markets significantly decreased when oil prices were lower. From this point of the view, domestic and international oil markets have the same characteristics. 3. The influence of domestic commodity markets on the Chinese crude oil market increased. After the crisis, Chinese soybean, corn, copper and gold markets had a direct or indirect impact on the Chinese crude oil market; in particular, a contemporaneous causality relationship between Chinese soybean and crude oil markets always exists. The reason is that China is a major producer and the largest importer of soybean, and soybean oil has a substitution effect on diesel as a raw material for biofuel. After the crisis, the decrease in biofuel cost affected by the decrease in soybean prices influenced the crude oil price. The significant impact of other domestic commodity markets on the crude oil market indicates that the stability of Chinese commodity markets was weakened by the financial crisis and the regulation policy exerted on one market may induce a similar reaction for other markets; in other words, oriented policies among the markets are obvious. However, only the Chinese crude oil spot market shows a price response lag in relation to the futures market. Thus, the Chinese crude oil price is influenced by other domestic futures markets. 4. The impacts of domestic commodity markets on international commodity markets strengthened, especially for agriculture and metal markets. Moreover, the internal links among agriculture markets strengthened while it is polarization in the metal markets. The relationship between copper and gold markets disappeared owing to differences in feedback to the financial crisis: the price of copper decreased because it is a common metal, but the price of gold increased as a hedging tool. In addition, the information transmission among markets strengthened after the crisis, which is reflected in a stronger relationship between agriculture and metal markets which does not exist before the crisis. By comparison, the Chinese crude oil market is always a price taker and is mainly affected by the international oil market, which is determined by the pricing mechanism for the market itself. However, the degree of marketization is relatively high for the Chinese fuel oil futures market and it has similar characteristics to international oil markets; the impact on domestic commodity markets is greater when oil prices were high before the crisis, compared to a weak impact when prices were lower after the crisis. 4. Conclusions This study revealed the impact of Chinese oil markets on domestic and international commodity markets by identifying information transmission among them. Some important conclusions can be drawn. The Chinese oil market is no longer fully closed; with the stronger degree of marketization, the relationship with international commodity markets has become closer. In particular, development of China’s oil futures market has led to a long-term equilibrium relationship between the Chinese oil market and other international and domestic commodity markets. This indicates that as global market integration has accelerated and commodity market mechanisms have improved, not only has the linkage of different domestic commodity markets been enhanced, but the relationship between domestic and international commodity markets has also become closer. In addition, the performance of China’s fuel oil and crude oil markets is not entirely consistent, and both have their own characteristics in terms of information transmission to and from other international and domestic commodity markets. China’s crude oil market is a price taker before and after the crisis and is directly affected by the international oil market. In contrast, China’s fuel oil market is not only influenced by international oil markets, but also has an important effect on China’s other commodity markets. The contrastive results indicates that the liquidity of China’s crude oil market is relatively lower owing to the lack of a futures market; thus, it is difficult to play a role in the international pricing mechanism and the impact on other commodity markets is also weak. Acknowledgements Supports from the National Natural Science Foundation of China under Grant No. 91546109, No. 71133005, No. 71203210 and No. 71503114 are acknowledged. The authors appreciate the weekly seminars at CEEP in CAS, from where the earlier draft of the paper got improved. References Awokuse, T.O., Bessler, D.A., 2003. Vector autoregression, policy analysis, and directed graphs: an application to the US economy. J. Appl. Econ. 6, 1–24. Baffes, J., 2007. Oil spills on other commodities. Resour. Policy 32, 126–134. Bessler, D.A., Yang, J., 2003. The structure of interdependence in international stock markets. J. Int. Money Finance 22 (2), 261–287. Chang, T.H., Su, H.M., 2010. The substitutive effect of biofuels on fossil fuels in the lower and higher crude oil price periods. Energy 35, 2807–2813. Chen, K.C., Chen, S.L., Wu, L.F., 2009. Price causal relations between China and the world oil markets. Global Finance J. 20 (2), 107–118. Cody, B.J., Mills, L.O., 1991. The role of commodity prices in formulating monetary policy. Rev. Econ. Stat. 73, 358–365.
Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009
JID: FRL 8
ARTICLE IN PRESS
[m3Gsc;August 12, 2016;15:51]
Q. Ji, Y. Fan / Finance Research Letters 000 (2016) 1–8
Ftiti, Z., Fatnassi, I., Tiwari, A.K., 2016. Neoclassical finance, behavioral finance and noise traders: assessment of gold–oil markets. Finance Res. Lett. 17, 33–40. Hammoudeh, S., Yuan, Y., 2008. Metal volatility in presence of oil and interest rate shocks. Energy Econ. 30, 606–620. Harri, A., Nalley, L., Hudson, D., 2009. The relationship between oil, exchange rates and commodity prices. J. Agr. Appl. Econ. 41 (2), 501–510. Ji, Q., 2012. System analysis approach for the identification of factors driving crude oil prices. Comput. Indus. Eng. 63 (3), 615–625. Ji, Q., Fan, Y., 2016. Evolution of the world crude oil market integration: A graph theory analysis. Energy Econ. 53, 90–100. Liu, L., Chen, C.C., Wan, J., 2013. Is world oil market “one great pool”? An example from China’s and international oil markets. Econ. Modell. 35, 364–373. Nazlioglu, S., 2011. World oil and agricultural commodity prices: evidence from nonlinear causality. Energy Policy 39, 2935–2943. Nazlioglu, S., Soytas, U., 2011. World oil prices and agricultural commodity prices: evidence from an emerging market. Energy Econ. 33, 488–496. Nazlioglu, S., Soytas, U., 2012. Oil price, agricultural commodity prices, and the dollar: a panel cointegration and causality analysis. Energy Econ. 34, 1098–1104. Pindyck, R., Rotemberg, J., 1990. The excess comovement of commodity prices. Econ. J. 100, 1173–1189. Reboredo, J.C., Ugolini, A., 2016. The impact of downward/upward oil price movements on metal prices. Resour. Policy 49, 129–141. Sari, R., Hammoudeh, S., Soytas, U., 2010. Dynamics of oil price, precious metal prices, and exchange rate. Energy Econ. 32, 351–362. Sieczka, P., Holyst, J.A., 2009. Correlations in commodity markets. Physica A 388, 1621–1630. Sims, C., 1980. Macroeconomics and reality. Econometrica 48, 1–48. Sprites, P., Glymour, C., Scheines, R., 20 0 0. Causation, Prediction, and Search. MIT Press, Cambridge, pp. 1–130. Swanson, N., Granger, C., 1997. Impulse response functions based on a causal approach to residual Orthogonalization in Vector Autoregression. J. Am. Stat. Assoc. 92, 357–367. Wu, H, Li, S, 2013. Volatility spillovers in China’s crude oil, corn and fuel ethanol markets. Energy Policy 62, 878–886. Yang, J., Guo, H., Wang, Z.J., 2006. International transmission of inflation among G-7 countries: a data-determined VAR analysis. J. Bank. Finance 30 (10), 2681–2700.
Please cite this article as: Q. Ji, Y. Fan, How do China’s oil markets affect other commodity markets both domestically and internationally? Finance Research Letters (2016), http://dx.doi.org/10.1016/j.frl.2016.08.009